Overview

Brought to you by YData

Dataset statistics

Number of variables37
Number of observations1670214
Missing cells11109336
Missing cells (%)18.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory471.5 MiB
Average record size in memory296.0 B

Variable types

Numeric19
Categorical17
Boolean1

Alerts

AMT_ANNUITY is highly overall correlated with AMT_APPLICATION and 2 other fieldsHigh correlation
AMT_APPLICATION is highly overall correlated with AMT_ANNUITY and 3 other fieldsHigh correlation
AMT_CREDIT is highly overall correlated with AMT_ANNUITY and 4 other fieldsHigh correlation
AMT_DOWN_PAYMENT is highly overall correlated with RATE_DOWN_PAYMENTHigh correlation
AMT_GOODS_PRICE is highly overall correlated with AMT_ANNUITY and 3 other fieldsHigh correlation
CHANNEL_TYPE is highly overall correlated with NAME_CONTRACT_TYPE and 2 other fieldsHigh correlation
CNT_PAYMENT is highly overall correlated with AMT_APPLICATION and 3 other fieldsHigh correlation
CODE_REJECT_REASON is highly overall correlated with NAME_CONTRACT_STATUSHigh correlation
DAYS_DECISION is highly overall correlated with DAYS_FIRST_DUE and 4 other fieldsHigh correlation
DAYS_FIRST_DRAWING is highly overall correlated with FLAG_LAST_APPL_PER_CONTRACT and 6 other fieldsHigh correlation
DAYS_FIRST_DUE is highly overall correlated with DAYS_DECISION and 10 other fieldsHigh correlation
DAYS_LAST_DUE is highly overall correlated with DAYS_DECISION and 6 other fieldsHigh correlation
DAYS_LAST_DUE_1ST_VERSION is highly overall correlated with AMT_CREDIT and 12 other fieldsHigh correlation
DAYS_TERMINATION is highly overall correlated with DAYS_DECISION and 10 other fieldsHigh correlation
FLAG_LAST_APPL_PER_CONTRACT is highly overall correlated with DAYS_FIRST_DRAWING and 8 other fieldsHigh correlation
NAME_CASH_LOAN_PURPOSE is highly overall correlated with NAME_CONTRACT_TYPE and 4 other fieldsHigh correlation
NAME_CONTRACT_STATUS is highly overall correlated with CODE_REJECT_REASON and 10 other fieldsHigh correlation
NAME_CONTRACT_TYPE is highly overall correlated with CHANNEL_TYPE and 13 other fieldsHigh correlation
NAME_GOODS_CATEGORY is highly overall correlated with NAME_CONTRACT_TYPE and 1 other fieldsHigh correlation
NAME_PAYMENT_TYPE is highly overall correlated with DAYS_FIRST_DRAWING and 1 other fieldsHigh correlation
NAME_PORTFOLIO is highly overall correlated with CHANNEL_TYPE and 12 other fieldsHigh correlation
NAME_PRODUCT_TYPE is highly overall correlated with NAME_CASH_LOAN_PURPOSE and 4 other fieldsHigh correlation
NAME_SELLER_INDUSTRY is highly overall correlated with NAME_CONTRACT_TYPE and 2 other fieldsHigh correlation
NAME_YIELD_GROUP is highly overall correlated with DAYS_FIRST_DRAWING and 7 other fieldsHigh correlation
NFLAG_INSURED_ON_APPROVAL is highly overall correlated with FLAG_LAST_APPL_PER_CONTRACT and 5 other fieldsHigh correlation
NFLAG_LAST_APPL_IN_DAY is highly overall correlated with FLAG_LAST_APPL_PER_CONTRACTHigh correlation
PRODUCT_COMBINATION is highly overall correlated with DAYS_FIRST_DRAWING and 11 other fieldsHigh correlation
RATE_DOWN_PAYMENT is highly overall correlated with AMT_DOWN_PAYMENTHigh correlation
RATE_INTEREST_PRIMARY is highly overall correlated with FLAG_LAST_APPL_PER_CONTRACT and 7 other fieldsHigh correlation
RATE_INTEREST_PRIVILEGED is highly overall correlated with CHANNEL_TYPE and 14 other fieldsHigh correlation
FLAG_LAST_APPL_PER_CONTRACT is highly imbalanced (95.4%)Imbalance
NFLAG_LAST_APPL_IN_DAY is highly imbalanced (96.6%)Imbalance
NAME_CASH_LOAN_PURPOSE is highly imbalanced (71.5%)Imbalance
CODE_REJECT_REASON is highly imbalanced (66.2%)Imbalance
NAME_GOODS_CATEGORY is highly imbalanced (52.9%)Imbalance
AMT_ANNUITY has 372235 (22.3%) missing valuesMissing
AMT_DOWN_PAYMENT has 895844 (53.6%) missing valuesMissing
AMT_GOODS_PRICE has 385515 (23.1%) missing valuesMissing
RATE_DOWN_PAYMENT has 895844 (53.6%) missing valuesMissing
RATE_INTEREST_PRIMARY has 1664263 (99.6%) missing valuesMissing
RATE_INTEREST_PRIVILEGED has 1664263 (99.6%) missing valuesMissing
NAME_TYPE_SUITE has 820405 (49.1%) missing valuesMissing
CNT_PAYMENT has 372230 (22.3%) missing valuesMissing
DAYS_FIRST_DRAWING has 673065 (40.3%) missing valuesMissing
DAYS_FIRST_DUE has 673065 (40.3%) missing valuesMissing
DAYS_LAST_DUE_1ST_VERSION has 673065 (40.3%) missing valuesMissing
DAYS_LAST_DUE has 673065 (40.3%) missing valuesMissing
DAYS_TERMINATION has 673065 (40.3%) missing valuesMissing
NFLAG_INSURED_ON_APPROVAL has 673065 (40.3%) missing valuesMissing
AMT_DOWN_PAYMENT is highly skewed (γ1 = 36.47657581)Skewed
SELLERPLACE_AREA is highly skewed (γ1 = 529.6202788)Skewed
SK_ID_PREV is uniformly distributedUniform
SK_ID_PREV has unique valuesUnique
AMT_APPLICATION has 392402 (23.5%) zerosZeros
AMT_CREDIT has 336768 (20.2%) zerosZeros
AMT_DOWN_PAYMENT has 369854 (22.1%) zerosZeros
RATE_DOWN_PAYMENT has 369854 (22.1%) zerosZeros
SELLERPLACE_AREA has 60523 (3.6%) zerosZeros
CNT_PAYMENT has 144985 (8.7%) zerosZeros

Reproduction

Analysis started2025-10-19 14:41:38.833018
Analysis finished2025-10-19 14:48:37.085827
Duration6 minutes and 58.25 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

SK_ID_PREV
Real number (ℝ)

Uniform  Unique 

Distinct1670214
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1923089.1
Minimum1000001
Maximum2845382
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 MiB
2025-10-19T14:48:37.255127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000001
5-th percentile1092565.3
Q11461857.2
median1923110.5
Q32384279.8
95-th percentile2753178.3
Maximum2845382
Range1845381
Interquartile range (IQR)922422.5

Descriptive statistics

Standard deviation532597.96
Coefficient of variation (CV)0.27694918
Kurtosis-1.1997524
Mean1923089.1
Median Absolute Deviation (MAD)461211.5
Skewness-0.00057313346
Sum3.2119704 × 1012
Variance2.8366059 × 1011
MonotonicityNot monotonic
2025-10-19T14:48:37.483464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20304951
 
< 0.1%
10358481
 
< 0.1%
15264981
 
< 0.1%
21488931
 
< 0.1%
24374291
 
< 0.1%
16245411
 
< 0.1%
20956021
 
< 0.1%
12030771
 
< 0.1%
28424261
 
< 0.1%
17585961
 
< 0.1%
Other values (1670204)1670204
> 99.9%
ValueCountFrequency (%)
10000011
< 0.1%
10000021
< 0.1%
10000031
< 0.1%
10000041
< 0.1%
10000051
< 0.1%
10000061
< 0.1%
10000071
< 0.1%
10000081
< 0.1%
10000091
< 0.1%
10000101
< 0.1%
ValueCountFrequency (%)
28453821
< 0.1%
28453811
< 0.1%
28453791
< 0.1%
28453781
< 0.1%
28453771
< 0.1%
28453731
< 0.1%
28453721
< 0.1%
28453701
< 0.1%
28453691
< 0.1%
28453681
< 0.1%

SK_ID_CURR
Real number (ℝ)

Distinct338857
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean278357.17
Minimum100001
Maximum456255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 MiB
2025-10-19T14:48:37.729134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100001
5-th percentile117930
Q1189329
median278714.5
Q3367514
95-th percentile438443
Maximum456255
Range356254
Interquartile range (IQR)178185

Descriptive statistics

Standard deviation102814.82
Coefficient of variation (CV)0.36936294
Kurtosis-1.199259
Mean278357.17
Median Absolute Deviation (MAD)89120.5
Skewness-0.0033025438
Sum4.6491605 × 1011
Variance1.0570888 × 1010
MonotonicityNot monotonic
2025-10-19T14:48:37.948720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18786877
 
< 0.1%
26568173
 
< 0.1%
17368072
 
< 0.1%
24241268
 
< 0.1%
20678367
 
< 0.1%
15636766
 
< 0.1%
38995064
 
< 0.1%
38217964
 
< 0.1%
19835563
 
< 0.1%
34516162
 
< 0.1%
Other values (338847)1669538
> 99.9%
ValueCountFrequency (%)
1000011
 
< 0.1%
1000021
 
< 0.1%
1000033
 
< 0.1%
1000041
 
< 0.1%
1000052
 
< 0.1%
1000069
< 0.1%
1000076
< 0.1%
1000085
< 0.1%
1000097
< 0.1%
1000101
 
< 0.1%
ValueCountFrequency (%)
4562558
< 0.1%
4562542
 
< 0.1%
4562532
 
< 0.1%
4562521
 
< 0.1%
4562511
 
< 0.1%
4562508
< 0.1%
4562492
 
< 0.1%
4562484
< 0.1%
4562475
< 0.1%
4562462
 
< 0.1%

NAME_CONTRACT_TYPE
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.7 MiB
Cash loans
747553 
Consumer loans
729151 
Revolving loans
193164 
XNA
 
346

Length

Max length15
Median length14
Mean length12.323057
Min length3

Characters and Unicode

Total characters20582142
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConsumer loans
2nd rowCash loans
3rd rowCash loans
4th rowCash loans
5th rowCash loans

Common Values

ValueCountFrequency (%)
Cash loans747553
44.8%
Consumer loans729151
43.7%
Revolving loans193164
 
11.6%
XNA346
 
< 0.1%

Length

2025-10-19T14:48:38.145834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-19T14:48:38.320932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
loans1669868
50.0%
cash747553
22.4%
consumer729151
21.8%
revolving193164
 
5.8%
xna346
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
s3146572
15.3%
o2592183
12.6%
n2592183
12.6%
a2417421
11.7%
l1863032
9.1%
1669868
8.1%
C1476704
7.2%
e922315
 
4.5%
h747553
 
3.6%
r729151
 
3.5%
Other values (9)2425160
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)20582142
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s3146572
15.3%
o2592183
12.6%
n2592183
12.6%
a2417421
11.7%
l1863032
9.1%
1669868
8.1%
C1476704
7.2%
e922315
 
4.5%
h747553
 
3.6%
r729151
 
3.5%
Other values (9)2425160
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)20582142
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s3146572
15.3%
o2592183
12.6%
n2592183
12.6%
a2417421
11.7%
l1863032
9.1%
1669868
8.1%
C1476704
7.2%
e922315
 
4.5%
h747553
 
3.6%
r729151
 
3.5%
Other values (9)2425160
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)20582142
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s3146572
15.3%
o2592183
12.6%
n2592183
12.6%
a2417421
11.7%
l1863032
9.1%
1669868
8.1%
C1476704
7.2%
e922315
 
4.5%
h747553
 
3.6%
r729151
 
3.5%
Other values (9)2425160
11.8%

AMT_ANNUITY
Real number (ℝ)

High correlation  Missing 

Distinct357959
Distinct (%)27.6%
Missing372235
Missing (%)22.3%
Infinite0
Infinite (%)0.0%
Mean15955.121
Minimum0
Maximum418058.15
Zeros1637
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.7 MiB
2025-10-19T14:48:38.522426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2726.595
Q16321.78
median11250
Q320658.42
95-th percentile45336.78
Maximum418058.15
Range418058.15
Interquartile range (IQR)14336.64

Descriptive statistics

Standard deviation14782.137
Coefficient of variation (CV)0.92648233
Kurtosis15.069832
Mean15955.121
Median Absolute Deviation (MAD)5979.195
Skewness2.6925715
Sum2.0709412 × 1010
Variance2.1851158 × 108
MonotonicityNot monotonic
2025-10-19T14:48:38.722275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225031865
 
1.9%
1125013974
 
0.8%
675013442
 
0.8%
900012496
 
0.7%
2250011903
 
0.7%
450010597
 
0.6%
135007171
 
0.4%
33754806
 
0.3%
78754674
 
0.3%
382504129
 
0.2%
Other values (357949)1182922
70.8%
(Missing)372235
 
22.3%
ValueCountFrequency (%)
01637
0.1%
579.781
 
< 0.1%
585.8551
 
< 0.1%
635.041
 
< 0.1%
637.651
 
< 0.1%
643.591
 
< 0.1%
646.5152
 
< 0.1%
656.731
 
< 0.1%
665.191
 
< 0.1%
672.9751
 
< 0.1%
ValueCountFrequency (%)
418058.1452
< 0.1%
417927.6452
< 0.1%
393868.6651
< 0.1%
357733.261
< 0.1%
3099421
< 0.1%
300425.4451
< 0.1%
298557.5852
< 0.1%
298427.0852
< 0.1%
2903581
< 0.1%
281027.251
< 0.1%

AMT_APPLICATION
Real number (ℝ)

High correlation  Zeros 

Distinct93885
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175233.86
Minimum0
Maximum6905160
Zeros392402
Zeros (%)23.5%
Negative0
Negative (%)0.0%
Memory size12.7 MiB
2025-10-19T14:48:38.919769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118720
median71046
Q3180360
95-th percentile787500
Maximum6905160
Range6905160
Interquartile range (IQR)161640

Descriptive statistics

Standard deviation292779.76
Coefficient of variation (CV)1.6707945
Kurtosis15.762243
Mean175233.86
Median Absolute Deviation (MAD)71046
Skewness3.3914422
Sum2.9267805 × 1011
Variance8.5719989 × 1010
MonotonicityNot monotonic
2025-10-19T14:48:39.135670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0392402
 
23.5%
4500047831
 
2.9%
22500043543
 
2.6%
13500040678
 
2.4%
45000038905
 
2.3%
9000029367
 
1.8%
18000024738
 
1.5%
27000020573
 
1.2%
67500020227
 
1.2%
6750016861
 
1.0%
Other values (93875)995089
59.6%
ValueCountFrequency (%)
0392402
23.5%
34561
 
< 0.1%
4225.51
 
< 0.1%
45004
 
< 0.1%
54001
 
< 0.1%
55351
 
< 0.1%
5575.51
 
< 0.1%
55801
 
< 0.1%
5710.51
 
< 0.1%
57151
 
< 0.1%
ValueCountFrequency (%)
69051601
 
< 0.1%
58500002
 
< 0.1%
50850001
 
< 0.1%
44550001
 
< 0.1%
42378753
 
< 0.1%
41850001
 
< 0.1%
41400001
 
< 0.1%
405000012
< 0.1%
40050001
 
< 0.1%
39825001
 
< 0.1%

AMT_CREDIT
Real number (ℝ)

High correlation  Zeros 

Distinct86803
Distinct (%)5.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean196114.02
Minimum0
Maximum6905160
Zeros336768
Zeros (%)20.2%
Negative0
Negative (%)0.0%
Memory size12.7 MiB
2025-10-19T14:48:39.361044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q124160.5
median80541
Q3216418.5
95-th percentile886500
Maximum6905160
Range6905160
Interquartile range (IQR)192258

Descriptive statistics

Standard deviation318574.62
Coefficient of variation (CV)1.6244357
Kurtosis14.238793
Mean196114.02
Median Absolute Deviation (MAD)80541
Skewness3.2458146
Sum3.2755219 × 1011
Variance1.0148979 × 1011
MonotonicityNot monotonic
2025-10-19T14:48:39.576227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0336768
 
20.2%
4500035051
 
2.1%
22500021094
 
1.3%
45000019954
 
1.2%
13500018720
 
1.1%
18000017085
 
1.0%
9000013781
 
0.8%
2700009842
 
0.6%
9000007432
 
0.4%
675007245
 
0.4%
Other values (86793)1183241
70.8%
ValueCountFrequency (%)
0336768
20.2%
34561
 
< 0.1%
4225.51
 
< 0.1%
45004
 
< 0.1%
51391
 
< 0.1%
5143.51
 
< 0.1%
5179.51
 
< 0.1%
53551
 
< 0.1%
55621
 
< 0.1%
55711
 
< 0.1%
ValueCountFrequency (%)
69051601
 
< 0.1%
4509688.51
 
< 0.1%
41043514
 
< 0.1%
40950001
 
< 0.1%
40855501
 
< 0.1%
405000012
< 0.1%
4045711.51
 
< 0.1%
40095001
 
< 0.1%
40050001
 
< 0.1%
38471043
 
< 0.1%

AMT_DOWN_PAYMENT
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct29278
Distinct (%)3.8%
Missing895844
Missing (%)53.6%
Infinite0
Infinite (%)0.0%
Mean6697.4021
Minimum-0.9
Maximum3060045
Zeros369854
Zeros (%)22.1%
Negative2
Negative (%)< 0.1%
Memory size12.7 MiB
2025-10-19T14:48:39.790356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.9
5-th percentile0
Q10
median1638
Q37740
95-th percentile26184.082
Maximum3060045
Range3060045.9
Interquartile range (IQR)7740

Descriptive statistics

Standard deviation20921.495
Coefficient of variation (CV)3.1238225
Kurtosis2901.845
Mean6697.4021
Median Absolute Deviation (MAD)1638
Skewness36.476576
Sum5.1862673 × 109
Variance4.3770897 × 108
MonotonicityNot monotonic
2025-10-19T14:48:40.001688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0369854
22.1%
450021241
 
1.3%
900014747
 
0.9%
135009655
 
0.6%
225008165
 
0.5%
67507709
 
0.5%
22506241
 
0.4%
180004526
 
0.3%
450004059
 
0.2%
27003362
 
0.2%
Other values (29268)324811
 
19.4%
(Missing)895844
53.6%
ValueCountFrequency (%)
-0.91
 
< 0.1%
-0.451
 
< 0.1%
0369854
22.1%
0.04537
 
< 0.1%
0.0940
 
< 0.1%
0.13528
 
< 0.1%
0.1839
 
< 0.1%
0.22554
 
< 0.1%
0.2736
 
< 0.1%
0.31518
 
< 0.1%
ValueCountFrequency (%)
30600451
 
< 0.1%
24750001
 
< 0.1%
21501001
 
< 0.1%
21357001
 
< 0.1%
2118937.53
< 0.1%
20340001
 
< 0.1%
20250001
 
< 0.1%
19800002
< 0.1%
19649701
 
< 0.1%
18000002
< 0.1%

AMT_GOODS_PRICE
Real number (ℝ)

High correlation  Missing 

Distinct93885
Distinct (%)7.3%
Missing385515
Missing (%)23.1%
Infinite0
Infinite (%)0.0%
Mean227847.28
Minimum0
Maximum6905160
Zeros6869
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size12.7 MiB
2025-10-19T14:48:40.212435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22882.5
Q150841
median112320
Q3234000
95-th percentile900000
Maximum6905160
Range6905160
Interquartile range (IQR)183159

Descriptive statistics

Standard deviation315396.56
Coefficient of variation (CV)1.3842454
Kurtosis12.86636
Mean227847.28
Median Absolute Deviation (MAD)69030
Skewness3.0736897
Sum2.9271517 × 1011
Variance9.9474989 × 1010
MonotonicityNot monotonic
2025-10-19T14:48:40.428492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4500047831
 
2.9%
22500043549
 
2.6%
13500040666
 
2.4%
45000038926
 
2.3%
9000029367
 
1.8%
18000024736
 
1.5%
27000020567
 
1.2%
67500020235
 
1.2%
6750016857
 
1.0%
90000015572
 
0.9%
Other values (93875)986393
59.1%
(Missing)385515
 
23.1%
ValueCountFrequency (%)
06869
0.4%
34561
 
< 0.1%
4225.51
 
< 0.1%
45004
 
< 0.1%
54001
 
< 0.1%
55351
 
< 0.1%
5575.51
 
< 0.1%
55801
 
< 0.1%
5710.51
 
< 0.1%
57151
 
< 0.1%
ValueCountFrequency (%)
69051601
 
< 0.1%
58500002
 
< 0.1%
50850001
 
< 0.1%
44550001
 
< 0.1%
42378753
 
< 0.1%
41850001
 
< 0.1%
41400001
 
< 0.1%
405000012
< 0.1%
40050001
 
< 0.1%
39825001
 
< 0.1%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.7 MiB
TUESDAY
255118 
WEDNESDAY
255010 
MONDAY
253557 
FRIDAY
252048 
THURSDAY
249099 
Other values (2)
405382 

Length

Max length9
Median length8
Mean length7.1972166
Min length6

Characters and Unicode

Total characters12020892
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSATURDAY
2nd rowTHURSDAY
3rd rowTUESDAY
4th rowMONDAY
5th rowTHURSDAY

Common Values

ValueCountFrequency (%)
TUESDAY255118
15.3%
WEDNESDAY255010
15.3%
MONDAY253557
15.2%
FRIDAY252048
15.1%
THURSDAY249099
14.9%
SATURDAY240631
14.4%
SUNDAY164751
9.9%

Length

2025-10-19T14:48:40.634329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-19T14:48:40.829930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
tuesday255118
15.3%
wednesday255010
15.3%
monday253557
15.2%
friday252048
15.1%
thursday249099
14.9%
saturday240631
14.4%
sunday164751
9.9%

Most occurring characters

ValueCountFrequency (%)
D1925224
16.0%
A1910845
15.9%
Y1670214
13.9%
S1164609
9.7%
U909599
7.6%
E765138
 
6.4%
T744848
 
6.2%
R741778
 
6.2%
N673318
 
5.6%
W255010
 
2.1%
Other values (5)1260309
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)12020892
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D1925224
16.0%
A1910845
15.9%
Y1670214
13.9%
S1164609
9.7%
U909599
7.6%
E765138
 
6.4%
T744848
 
6.2%
R741778
 
6.2%
N673318
 
5.6%
W255010
 
2.1%
Other values (5)1260309
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12020892
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D1925224
16.0%
A1910845
15.9%
Y1670214
13.9%
S1164609
9.7%
U909599
7.6%
E765138
 
6.4%
T744848
 
6.2%
R741778
 
6.2%
N673318
 
5.6%
W255010
 
2.1%
Other values (5)1260309
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12020892
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D1925224
16.0%
A1910845
15.9%
Y1670214
13.9%
S1164609
9.7%
U909599
7.6%
E765138
 
6.4%
T744848
 
6.2%
R741778
 
6.2%
N673318
 
5.6%
W255010
 
2.1%
Other values (5)1260309
10.5%

HOUR_APPR_PROCESS_START
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.484182
Minimum0
Maximum23
Zeros109
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size12.7 MiB
2025-10-19T14:48:41.039844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q110
median12
Q315
95-th percentile18
Maximum23
Range23
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.334028
Coefficient of variation (CV)0.26706019
Kurtosis-0.27776836
Mean12.484182
Median Absolute Deviation (MAD)2
Skewness-0.025629249
Sum20851255
Variance11.115742
MonotonicityNot monotonic
2025-10-19T14:48:41.207408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
11192728
11.5%
12185980
11.1%
10181690
10.9%
13172256
10.3%
14157711
9.4%
15142965
8.6%
9127002
7.6%
16121361
7.3%
1795064
5.7%
873085
 
4.4%
Other values (14)220372
13.2%
ValueCountFrequency (%)
0109
 
< 0.1%
1212
 
< 0.1%
21116
 
0.1%
35035
 
0.3%
49319
 
0.6%
515392
 
0.9%
625759
 
1.5%
745646
 
2.7%
873085
4.4%
9127002
7.6%
ValueCountFrequency (%)
23202
 
< 0.1%
22720
 
< 0.1%
214082
 
0.2%
2014535
 
0.9%
1934089
 
2.0%
1864156
3.8%
1795064
5.7%
16121361
7.3%
15142965
8.6%
14157711
9.4%

FLAG_LAST_APPL_PER_CONTRACT
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
True
1661739 
False
 
8475
ValueCountFrequency (%)
True1661739
99.5%
False8475
 
0.5%
2025-10-19T14:48:41.363851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

NFLAG_LAST_APPL_IN_DAY
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.7 MiB
1
1664314 
0
 
5900

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1670214
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
11664314
99.6%
05900
 
0.4%

Length

2025-10-19T14:48:41.522433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-19T14:48:41.666687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11664314
99.6%
05900
 
0.4%

Most occurring characters

ValueCountFrequency (%)
11664314
99.6%
05900
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)1670214
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11664314
99.6%
05900
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1670214
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11664314
99.6%
05900
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1670214
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11664314
99.6%
05900
 
0.4%

RATE_DOWN_PAYMENT
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct207033
Distinct (%)26.7%
Missing895844
Missing (%)53.6%
Infinite0
Infinite (%)0.0%
Mean0.079636815
Minimum-1.4978763 × 10-5
Maximum1
Zeros369854
Zeros (%)22.1%
Negative2
Negative (%)< 0.1%
Memory size12.7 MiB
2025-10-19T14:48:41.844990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.4978763 × 10-5
5-th percentile0
Q10
median0.051605085
Q30.10890909
95-th percentile0.29412643
Maximum1
Range1.000015
Interquartile range (IQR)0.10890909

Descriptive statistics

Standard deviation0.10782331
Coefficient of variation (CV)1.353938
Kurtosis6.2044689
Mean0.079636815
Median Absolute Deviation (MAD)0.051605085
Skewness2.1077129
Sum61668.361
Variance0.011625867
MonotonicityNot monotonic
2025-10-19T14:48:42.066723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0369854
22.1%
0.108909090936341
 
2.2%
0.21781818186482
 
0.4%
0.32672727271081
 
0.1%
0.5445454545746
 
< 0.1%
0.4356363636449
 
< 0.1%
0.1042746615304
 
< 0.1%
0.1013781431258
 
< 0.1%
0.09946035699252
 
< 0.1%
0.1000083479243
 
< 0.1%
Other values (207023)358360
21.5%
(Missing)895844
53.6%
ValueCountFrequency (%)
-1.497876341 × 10-51
 
< 0.1%
-1.369340042 × 10-51
 
< 0.1%
0369854
22.1%
1.554444933 × 10-71
 
< 0.1%
2.102938266 × 10-71
 
< 0.1%
2.168812116 × 10-71
 
< 0.1%
2.36993259 × 10-71
 
< 0.1%
2.397769026 × 10-71
 
< 0.1%
2.548534315 × 10-71
 
< 0.1%
2.723407444 × 10-71
 
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.98973987761
< 0.1%
0.98071526451
< 0.1%
0.98018181821
< 0.1%
0.9725503051
< 0.1%
0.96034116961
< 0.1%
0.95885573851
< 0.1%
0.94844492991
< 0.1%
0.94749329941
< 0.1%
0.94477645061
< 0.1%

RATE_INTEREST_PRIMARY
Real number (ℝ)

High correlation  Missing 

Distinct148
Distinct (%)2.5%
Missing1664263
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean0.18835689
Minimum0.034781254
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 MiB
2025-10-19T14:48:42.284596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.034781254
5-th percentile0.14244021
Q10.16071631
median0.18912218
Q30.19332993
95-th percentile0.19691431
Maximum1
Range0.96521875
Interquartile range (IQR)0.032613623

Descriptive statistics

Standard deviation0.087671105
Coefficient of variation (CV)0.46545207
Kurtosis28.204535
Mean0.18835689
Median Absolute Deviation (MAD)0.0077779667
Skewness5.198204
Sum1120.9118
Variance0.0076862226
MonotonicityNot monotonic
2025-10-19T14:48:42.500031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.18913634821218
 
0.1%
0.1424402131951
 
0.1%
0.1607163096821
 
< 0.1%
0.1933299331681
 
< 0.1%
0.1969001473573
 
< 0.1%
0.1760030602241
 
< 0.1%
0.1891221807210
 
< 0.1%
0.1607021421204
 
< 0.1%
0.1828176357187
 
< 0.1%
0.1969143149139
 
< 0.1%
Other values (138)726
 
< 0.1%
(Missing)1664263
99.6%
ValueCountFrequency (%)
0.034781253541
 
< 0.1%
0.059121047262
 
< 0.1%
0.0591352147861
< 0.1%
0.05914938232
 
< 0.1%
0.095772413012
 
< 0.1%
0.10373455742
 
< 0.1%
0.11547942881
 
< 0.1%
0.12096225771
 
< 0.1%
0.12763515811
 
< 0.1%
0.127833503317
 
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.90292417551
< 0.1%
0.86130001131
< 0.1%
0.81551059731
< 0.1%
0.80676923951
< 0.1%
0.79744701351
< 0.1%
0.77419811861
< 0.1%
0.74498469911
< 0.1%
0.74358211491
< 0.1%
0.73999773321
< 0.1%

RATE_INTEREST_PRIVILEGED
Real number (ℝ)

High correlation  Missing 

Distinct25
Distinct (%)0.4%
Missing1664263
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean0.77350254
Minimum0.37315011
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 MiB
2025-10-19T14:48:42.681324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.37315011
5-th percentile0.63794926
Q10.71564482
median0.83509514
Q30.852537
95-th percentile0.86733615
Maximum1
Range0.62684989
Interquartile range (IQR)0.13689218

Descriptive statistics

Standard deviation0.10087859
Coefficient of variation (CV)0.13041792
Kurtosis0.25557607
Mean0.77350254
Median Absolute Deviation (MAD)0.032241015
Skewness-1.00768
Sum4603.1136
Variance0.01017649
MonotonicityNot monotonic
2025-10-19T14:48:42.861771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.83509513741717
 
0.1%
0.71564482031046
 
0.1%
0.637949261039
 
0.1%
0.8673361522931
 
0.1%
0.8525369979876
 
0.1%
0.5687103594127
 
< 0.1%
0.424418604766
 
< 0.1%
0.513742071945
 
< 0.1%
0.832452431340
 
< 0.1%
0.845137420719
 
< 0.1%
Other values (15)45
 
< 0.1%
(Missing)1664263
99.6%
ValueCountFrequency (%)
0.37315010572
 
< 0.1%
0.424418604766
< 0.1%
0.43657505292
 
< 0.1%
0.48414376321
 
< 0.1%
0.50211416491
 
< 0.1%
0.513742071945
 
< 0.1%
0.54281183931
 
< 0.1%
0.54809725161
 
< 0.1%
0.5687103594127
< 0.1%
0.63742071887
 
< 0.1%
ValueCountFrequency (%)
11
 
< 0.1%
0.8673361522931
0.1%
0.85465116281
 
< 0.1%
0.8525369979876
0.1%
0.845137420719
 
< 0.1%
0.83509513741717
0.1%
0.832452431340
 
< 0.1%
0.82082452431
 
< 0.1%
0.80655391124
 
< 0.1%
0.79069767445
 
< 0.1%

NAME_CASH_LOAN_PURPOSE
Categorical

High correlation  Imbalance 

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.7 MiB
XAP
922661 
XNA
677918 
Repairs
 
23765
Other
 
15608
Urgent needs
 
8412
Other values (20)
 
21850

Length

Max length32
Median length3
Mean length3.3122025
Min length3

Characters and Unicode

Total characters5532087
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowXAP
2nd rowXNA
3rd rowXNA
4th rowXNA
5th rowRepairs

Common Values

ValueCountFrequency (%)
XAP922661
55.2%
XNA677918
40.6%
Repairs23765
 
1.4%
Other15608
 
0.9%
Urgent needs8412
 
0.5%
Buying a used car2888
 
0.2%
Building a house or an annex2693
 
0.2%
Everyday expenses2416
 
0.1%
Medicine2174
 
0.1%
Payments on other loans1931
 
0.1%
Other values (15)9748
 
0.6%

Length

2025-10-19T14:48:43.068194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
xap922661
53.5%
xna677918
39.3%
repairs24562
 
1.4%
other17539
 
1.0%
urgent8412
 
0.5%
needs8412
 
0.5%
a8152
 
0.5%
buying5434
 
0.3%
car4697
 
0.3%
used2888
 
0.2%
Other values (41)45306
 
2.6%

Most occurring characters

ValueCountFrequency (%)
X1600579
28.9%
A1600579
28.9%
P925653
16.7%
N677918
12.3%
e104454
 
1.9%
r66486
 
1.2%
55767
 
1.0%
a54954
 
1.0%
n52820
 
1.0%
s50228
 
0.9%
Other values (31)342649
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)5532087
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
X1600579
28.9%
A1600579
28.9%
P925653
16.7%
N677918
12.3%
e104454
 
1.9%
r66486
 
1.2%
55767
 
1.0%
a54954
 
1.0%
n52820
 
1.0%
s50228
 
0.9%
Other values (31)342649
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5532087
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
X1600579
28.9%
A1600579
28.9%
P925653
16.7%
N677918
12.3%
e104454
 
1.9%
r66486
 
1.2%
55767
 
1.0%
a54954
 
1.0%
n52820
 
1.0%
s50228
 
0.9%
Other values (31)342649
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5532087
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
X1600579
28.9%
A1600579
28.9%
P925653
16.7%
N677918
12.3%
e104454
 
1.9%
r66486
 
1.2%
55767
 
1.0%
a54954
 
1.0%
n52820
 
1.0%
s50228
 
0.9%
Other values (31)342649
 
6.2%

NAME_CONTRACT_STATUS
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.7 MiB
Approved
1036781 
Canceled
316319 
Refused
290678 
Unused offer
 
26436

Length

Max length12
Median length8
Mean length7.8892753
Min length7

Characters and Unicode

Total characters13176778
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApproved
2nd rowApproved
3rd rowApproved
4th rowApproved
5th rowRefused

Common Values

ValueCountFrequency (%)
Approved1036781
62.1%
Canceled316319
 
18.9%
Refused290678
 
17.4%
Unused offer26436
 
1.6%

Length

2025-10-19T14:48:43.249981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-19T14:48:43.421381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
approved1036781
61.1%
canceled316319
 
18.6%
refused290678
 
17.1%
unused26436
 
1.6%
offer26436
 
1.6%

Most occurring characters

ValueCountFrequency (%)
e2303647
17.5%
p2073562
15.7%
d1670214
12.7%
r1063217
8.1%
o1063217
8.1%
A1036781
7.9%
v1036781
7.9%
f343550
 
2.6%
n342755
 
2.6%
u317114
 
2.4%
Other values (8)1925940
14.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)13176778
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e2303647
17.5%
p2073562
15.7%
d1670214
12.7%
r1063217
8.1%
o1063217
8.1%
A1036781
7.9%
v1036781
7.9%
f343550
 
2.6%
n342755
 
2.6%
u317114
 
2.4%
Other values (8)1925940
14.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13176778
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e2303647
17.5%
p2073562
15.7%
d1670214
12.7%
r1063217
8.1%
o1063217
8.1%
A1036781
7.9%
v1036781
7.9%
f343550
 
2.6%
n342755
 
2.6%
u317114
 
2.4%
Other values (8)1925940
14.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13176778
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e2303647
17.5%
p2073562
15.7%
d1670214
12.7%
r1063217
8.1%
o1063217
8.1%
A1036781
7.9%
v1036781
7.9%
f343550
 
2.6%
n342755
 
2.6%
u317114
 
2.4%
Other values (8)1925940
14.6%

DAYS_DECISION
Real number (ℝ)

High correlation 

Distinct2922
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-880.67967
Minimum-2922
Maximum-1
Zeros0
Zeros (%)0.0%
Negative1670214
Negative (%)100.0%
Memory size12.7 MiB
2025-10-19T14:48:43.614037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2922
5-th percentile-2559
Q1-1300
median-581
Q3-280
95-th percentile-85
Maximum-1
Range2921
Interquartile range (IQR)1020

Descriptive statistics

Standard deviation779.09967
Coefficient of variation (CV)-0.88465727
Kurtosis-0.037845835
Mean-880.67967
Median Absolute Deviation (MAD)375
Skewness-1.0530797
Sum-1.4709235 × 109
Variance606996.29
MonotonicityNot monotonic
2025-10-19T14:48:43.825791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2452444
 
0.1%
-2382390
 
0.1%
-2102375
 
0.1%
-2732350
 
0.1%
-1962315
 
0.1%
-2242305
 
0.1%
-2522300
 
0.1%
-1822283
 
0.1%
-2402279
 
0.1%
-2312270
 
0.1%
Other values (2912)1646903
98.6%
ValueCountFrequency (%)
-2922162
< 0.1%
-2921158
< 0.1%
-2920168
< 0.1%
-2919171
< 0.1%
-2918185
< 0.1%
-2917174
< 0.1%
-2916166
< 0.1%
-2915169
< 0.1%
-2914192
< 0.1%
-2913188
< 0.1%
ValueCountFrequency (%)
-12
 
< 0.1%
-21172
0.1%
-31516
0.1%
-41507
0.1%
-51324
0.1%
-61363
0.1%
-71697
0.1%
-81399
0.1%
-91228
0.1%
-101138
0.1%

NAME_PAYMENT_TYPE
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.7 MiB
Cash through the bank
1033552 
XNA
627384 
Non-cash from your account
 
8193
Cashless from the account of the employer
 
1085

Length

Max length41
Median length21
Mean length14.276163
Min length3

Characters and Unicode

Total characters23844247
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCash through the bank
2nd rowXNA
3rd rowCash through the bank
4th rowCash through the bank
5th rowCash through the bank

Common Values

ValueCountFrequency (%)
Cash through the bank1033552
61.9%
XNA627384
37.6%
Non-cash from your account8193
 
0.5%
Cashless from the account of the employer1085
 
0.1%

Length

2025-10-19T14:48:44.039881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-19T14:48:44.217648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
the1035722
21.6%
cash1033552
21.5%
through1033552
21.5%
bank1033552
21.5%
xna627384
13.1%
from9278
 
0.2%
account9278
 
0.2%
non-cash8193
 
0.2%
your8193
 
0.2%
cashless1085
 
< 0.1%
Other values (2)2170
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
h4145656
17.4%
3131745
13.1%
a2085660
 
8.7%
t2078552
 
8.7%
o1070664
 
4.5%
r1052108
 
4.4%
n1051023
 
4.4%
u1051023
 
4.4%
s1045000
 
4.4%
e1038977
 
4.4%
Other values (14)6093839
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)23844247
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
h4145656
17.4%
3131745
13.1%
a2085660
 
8.7%
t2078552
 
8.7%
o1070664
 
4.5%
r1052108
 
4.4%
n1051023
 
4.4%
u1051023
 
4.4%
s1045000
 
4.4%
e1038977
 
4.4%
Other values (14)6093839
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)23844247
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
h4145656
17.4%
3131745
13.1%
a2085660
 
8.7%
t2078552
 
8.7%
o1070664
 
4.5%
r1052108
 
4.4%
n1051023
 
4.4%
u1051023
 
4.4%
s1045000
 
4.4%
e1038977
 
4.4%
Other values (14)6093839
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)23844247
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
h4145656
17.4%
3131745
13.1%
a2085660
 
8.7%
t2078552
 
8.7%
o1070664
 
4.5%
r1052108
 
4.4%
n1051023
 
4.4%
u1051023
 
4.4%
s1045000
 
4.4%
e1038977
 
4.4%
Other values (14)6093839
25.6%

CODE_REJECT_REASON
Categorical

High correlation  Imbalance 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.7 MiB
XAP
1353093 
HC
175231 
LIMIT
 
55680
SCO
 
37467
CLIENT
 
26436
Other values (4)
 
22307

Length

Max length6
Median length3
Mean length3.0301039
Min length2

Characters and Unicode

Total characters5060922
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowXAP
2nd rowXAP
3rd rowXAP
4th rowXAP
5th rowHC

Common Values

ValueCountFrequency (%)
XAP1353093
81.0%
HC175231
 
10.5%
LIMIT55680
 
3.3%
SCO37467
 
2.2%
CLIENT26436
 
1.6%
SCOFR12811
 
0.8%
XNA5244
 
0.3%
VERIF3535
 
0.2%
SYSTEM717
 
< 0.1%

Length

2025-10-19T14:48:44.427292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-19T14:48:44.624139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
xap1353093
81.0%
hc175231
 
10.5%
limit55680
 
3.3%
sco37467
 
2.2%
client26436
 
1.6%
scofr12811
 
0.8%
xna5244
 
0.3%
verif3535
 
0.2%
system717
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
X1358337
26.8%
A1358337
26.8%
P1353093
26.7%
C251945
 
5.0%
H175231
 
3.5%
I141331
 
2.8%
T82833
 
1.6%
L82116
 
1.6%
M56397
 
1.1%
S51712
 
1.0%
Other values (7)149590
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)5060922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
X1358337
26.8%
A1358337
26.8%
P1353093
26.7%
C251945
 
5.0%
H175231
 
3.5%
I141331
 
2.8%
T82833
 
1.6%
L82116
 
1.6%
M56397
 
1.1%
S51712
 
1.0%
Other values (7)149590
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5060922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
X1358337
26.8%
A1358337
26.8%
P1353093
26.7%
C251945
 
5.0%
H175231
 
3.5%
I141331
 
2.8%
T82833
 
1.6%
L82116
 
1.6%
M56397
 
1.1%
S51712
 
1.0%
Other values (7)149590
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5060922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
X1358337
26.8%
A1358337
26.8%
P1353093
26.7%
C251945
 
5.0%
H175231
 
3.5%
I141331
 
2.8%
T82833
 
1.6%
L82116
 
1.6%
M56397
 
1.1%
S51712
 
1.0%
Other values (7)149590
 
3.0%

NAME_TYPE_SUITE
Categorical

Missing 

Distinct7
Distinct (%)< 0.1%
Missing820405
Missing (%)49.1%
Memory size12.7 MiB
Unaccompanied
508970 
Family
213263 
Spouse, partner
67069 
Children
 
31566
Other_B
 
17624
Other values (2)
 
11317

Length

Max length15
Median length13
Mean length11.032194
Min length6

Characters and Unicode

Total characters9375258
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnaccompanied
2nd rowSpouse, partner
3rd rowFamily
4th rowUnaccompanied
5th rowUnaccompanied

Common Values

ValueCountFrequency (%)
Unaccompanied508970
30.5%
Family213263
 
12.8%
Spouse, partner67069
 
4.0%
Children31566
 
1.9%
Other_B17624
 
1.1%
Other_A9077
 
0.5%
Group of people2240
 
0.1%
(Missing)820405
49.1%

Length

2025-10-19T14:48:44.824501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-19T14:48:44.994521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
unaccompanied508970
55.2%
family213263
23.1%
spouse67069
 
7.3%
partner67069
 
7.3%
children31566
 
3.4%
other_b17624
 
1.9%
other_a9077
 
1.0%
group2240
 
0.2%
of2240
 
0.2%
people2240
 
0.2%

Most occurring characters

ValueCountFrequency (%)
a1298272
13.8%
n1116575
11.9%
c1017940
10.9%
i753799
8.0%
m722233
7.7%
e705855
7.5%
p649828
6.9%
o582759
 
6.2%
d540536
 
5.8%
U508970
 
5.4%
Other values (18)1478491
15.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)9375258
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a1298272
13.8%
n1116575
11.9%
c1017940
10.9%
i753799
8.0%
m722233
7.7%
e705855
7.5%
p649828
6.9%
o582759
 
6.2%
d540536
 
5.8%
U508970
 
5.4%
Other values (18)1478491
15.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9375258
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a1298272
13.8%
n1116575
11.9%
c1017940
10.9%
i753799
8.0%
m722233
7.7%
e705855
7.5%
p649828
6.9%
o582759
 
6.2%
d540536
 
5.8%
U508970
 
5.4%
Other values (18)1478491
15.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9375258
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a1298272
13.8%
n1116575
11.9%
c1017940
10.9%
i753799
8.0%
m722233
7.7%
e705855
7.5%
p649828
6.9%
o582759
 
6.2%
d540536
 
5.8%
U508970
 
5.4%
Other values (18)1478491
15.8%

NAME_CLIENT_TYPE
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.7 MiB
Repeater
1231261 
New
301363 
Refreshed
135649 
XNA
 
1941

Length

Max length9
Median length8
Mean length7.1732371
Min length3

Characters and Unicode

Total characters11980841
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRepeater
2nd rowRepeater
3rd rowRepeater
4th rowRepeater
5th rowRepeater

Common Values

ValueCountFrequency (%)
Repeater1231261
73.7%
New301363
 
18.0%
Refreshed135649
 
8.1%
XNA1941
 
0.1%

Length

2025-10-19T14:48:45.202151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-19T14:48:45.378419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
repeater1231261
73.7%
new301363
 
18.0%
refreshed135649
 
8.1%
xna1941
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e4402093
36.7%
R1366910
 
11.4%
r1366910
 
11.4%
p1231261
 
10.3%
a1231261
 
10.3%
t1231261
 
10.3%
N303304
 
2.5%
w301363
 
2.5%
f135649
 
1.1%
s135649
 
1.1%
Other values (4)275180
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)11980841
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e4402093
36.7%
R1366910
 
11.4%
r1366910
 
11.4%
p1231261
 
10.3%
a1231261
 
10.3%
t1231261
 
10.3%
N303304
 
2.5%
w301363
 
2.5%
f135649
 
1.1%
s135649
 
1.1%
Other values (4)275180
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11980841
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e4402093
36.7%
R1366910
 
11.4%
r1366910
 
11.4%
p1231261
 
10.3%
a1231261
 
10.3%
t1231261
 
10.3%
N303304
 
2.5%
w301363
 
2.5%
f135649
 
1.1%
s135649
 
1.1%
Other values (4)275180
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11980841
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e4402093
36.7%
R1366910
 
11.4%
r1366910
 
11.4%
p1231261
 
10.3%
a1231261
 
10.3%
t1231261
 
10.3%
N303304
 
2.5%
w301363
 
2.5%
f135649
 
1.1%
s135649
 
1.1%
Other values (4)275180
 
2.3%

NAME_GOODS_CATEGORY
Categorical

High correlation  Imbalance 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.7 MiB
XNA
950809 
Mobile
224708 
Consumer Electronics
121576 
Computers
105769 
Audio/Video
99441 
Other values (23)
167911 

Length

Max length24
Median length3
Mean length6.786328
Min length3

Characters and Unicode

Total characters11334620
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowMobile
2nd rowXNA
3rd rowXNA
4th rowXNA
5th rowXNA

Common Values

ValueCountFrequency (%)
XNA950809
56.9%
Mobile224708
 
13.5%
Consumer Electronics121576
 
7.3%
Computers105769
 
6.3%
Audio/Video99441
 
6.0%
Furniture53656
 
3.2%
Photo / Cinema Equipment25021
 
1.5%
Construction Materials24995
 
1.5%
Clothing and Accessories23554
 
1.4%
Auto Accessories7381
 
0.4%
Other values (18)33304
 
2.0%

Length

2025-10-19T14:48:45.565823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
xna950809
48.5%
mobile224708
 
11.5%
consumer121576
 
6.2%
electronics121576
 
6.2%
computers105769
 
5.4%
audio/video99441
 
5.1%
furniture53656
 
2.7%
accessories30935
 
1.6%
and26535
 
1.4%
equipment25021
 
1.3%
Other values (30)199024
 
10.2%

Most occurring characters

ValueCountFrequency (%)
A1091028
 
9.6%
X950809
 
8.4%
N950809
 
8.4%
o944391
 
8.3%
e921134
 
8.1%
i780621
 
6.9%
r561953
 
5.0%
s511856
 
4.5%
u500151
 
4.4%
n456800
 
4.0%
Other values (33)3665068
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)11334620
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A1091028
 
9.6%
X950809
 
8.4%
N950809
 
8.4%
o944391
 
8.3%
e921134
 
8.1%
i780621
 
6.9%
r561953
 
5.0%
s511856
 
4.5%
u500151
 
4.4%
n456800
 
4.0%
Other values (33)3665068
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11334620
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A1091028
 
9.6%
X950809
 
8.4%
N950809
 
8.4%
o944391
 
8.3%
e921134
 
8.1%
i780621
 
6.9%
r561953
 
5.0%
s511856
 
4.5%
u500151
 
4.4%
n456800
 
4.0%
Other values (33)3665068
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11334620
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A1091028
 
9.6%
X950809
 
8.4%
N950809
 
8.4%
o944391
 
8.3%
e921134
 
8.1%
i780621
 
6.9%
r561953
 
5.0%
s511856
 
4.5%
u500151
 
4.4%
n456800
 
4.0%
Other values (33)3665068
32.3%

NAME_PORTFOLIO
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.7 MiB
POS
691011 
Cash
461563 
XNA
372230 
Cards
144985 
Cars
 
425

Length

Max length5
Median length3
Mean length3.4502166
Min length3

Characters and Unicode

Total characters5762600
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPOS
2nd rowCash
3rd rowCash
4th rowCash
5th rowCash

Common Values

ValueCountFrequency (%)
POS691011
41.4%
Cash461563
27.6%
XNA372230
22.3%
Cards144985
 
8.7%
Cars425
 
< 0.1%

Length

2025-10-19T14:48:45.750053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-19T14:48:46.737907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
pos691011
41.4%
cash461563
27.6%
xna372230
22.3%
cards144985
 
8.7%
cars425
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
P691011
12.0%
O691011
12.0%
S691011
12.0%
C606973
10.5%
a606973
10.5%
s606973
10.5%
h461563
8.0%
X372230
6.5%
N372230
6.5%
A372230
6.5%
Other values (2)290395
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)5762600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P691011
12.0%
O691011
12.0%
S691011
12.0%
C606973
10.5%
a606973
10.5%
s606973
10.5%
h461563
8.0%
X372230
6.5%
N372230
6.5%
A372230
6.5%
Other values (2)290395
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5762600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P691011
12.0%
O691011
12.0%
S691011
12.0%
C606973
10.5%
a606973
10.5%
s606973
10.5%
h461563
8.0%
X372230
6.5%
N372230
6.5%
A372230
6.5%
Other values (2)290395
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5762600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P691011
12.0%
O691011
12.0%
S691011
12.0%
C606973
10.5%
a606973
10.5%
s606973
10.5%
h461563
8.0%
X372230
6.5%
N372230
6.5%
A372230
6.5%
Other values (2)290395
5.0%

NAME_PRODUCT_TYPE
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.7 MiB
XNA
1063666 
x-sell
456287 
walk-in
150261 

Length

Max length7
Median length3
Mean length4.1794327
Min length3

Characters and Unicode

Total characters6980547
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowXNA
2nd rowx-sell
3rd rowx-sell
4th rowx-sell
5th rowwalk-in

Common Values

ValueCountFrequency (%)
XNA1063666
63.7%
x-sell456287
27.3%
walk-in150261
 
9.0%

Length

2025-10-19T14:48:46.945520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-19T14:48:47.121805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
xna1063666
63.7%
x-sell456287
27.3%
walk-in150261
 
9.0%

Most occurring characters

ValueCountFrequency (%)
X1063666
15.2%
N1063666
15.2%
A1063666
15.2%
l1062835
15.2%
-606548
8.7%
x456287
6.5%
s456287
6.5%
e456287
6.5%
w150261
 
2.2%
a150261
 
2.2%
Other values (3)450783
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)6980547
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
X1063666
15.2%
N1063666
15.2%
A1063666
15.2%
l1062835
15.2%
-606548
8.7%
x456287
6.5%
s456287
6.5%
e456287
6.5%
w150261
 
2.2%
a150261
 
2.2%
Other values (3)450783
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6980547
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
X1063666
15.2%
N1063666
15.2%
A1063666
15.2%
l1062835
15.2%
-606548
8.7%
x456287
6.5%
s456287
6.5%
e456287
6.5%
w150261
 
2.2%
a150261
 
2.2%
Other values (3)450783
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6980547
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
X1063666
15.2%
N1063666
15.2%
A1063666
15.2%
l1062835
15.2%
-606548
8.7%
x456287
6.5%
s456287
6.5%
e456287
6.5%
w150261
 
2.2%
a150261
 
2.2%
Other values (3)450783
6.5%

CHANNEL_TYPE
Categorical

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.7 MiB
Credit and cash offices
719968 
Country-wide
494690 
Stone
212083 
Regional / Local
108528 
Contact center
 
71297
Other values (3)
 
63648

Length

Max length26
Median length23
Mean length16.351602
Min length5

Characters and Unicode

Total characters27310675
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCountry-wide
2nd rowContact center
3rd rowCredit and cash offices
4th rowCredit and cash offices
5th rowCredit and cash offices

Common Values

ValueCountFrequency (%)
Credit and cash offices719968
43.1%
Country-wide494690
29.6%
Stone212083
 
12.7%
Regional / Local108528
 
6.5%
Contact center71297
 
4.3%
AP+ (Cash loan)57046
 
3.4%
Channel of corporate sales6150
 
0.4%
Car dealer452
 
< 0.1%

Length

2025-10-19T14:48:47.299285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-19T14:48:47.494290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
cash777014
18.3%
credit719968
16.9%
offices719968
16.9%
and719968
16.9%
country-wide494690
11.6%
stone212083
 
5.0%
regional108528
 
2.6%
108528
 
2.6%
local108528
 
2.6%
center71297
 
1.7%
Other values (9)210893
 
5.0%

Most occurring characters

ValueCountFrequency (%)
2581251
 
9.5%
e2417185
 
8.9%
i2043154
 
7.5%
d1935078
 
7.1%
a1861735
 
6.8%
o1790590
 
6.6%
n1747209
 
6.4%
c1697208
 
6.2%
t1646782
 
6.0%
s1509282
 
5.5%
Other values (20)8081201
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)27310675
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2581251
 
9.5%
e2417185
 
8.9%
i2043154
 
7.5%
d1935078
 
7.1%
a1861735
 
6.8%
o1790590
 
6.6%
n1747209
 
6.4%
c1697208
 
6.2%
t1646782
 
6.0%
s1509282
 
5.5%
Other values (20)8081201
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)27310675
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2581251
 
9.5%
e2417185
 
8.9%
i2043154
 
7.5%
d1935078
 
7.1%
a1861735
 
6.8%
o1790590
 
6.6%
n1747209
 
6.4%
c1697208
 
6.2%
t1646782
 
6.0%
s1509282
 
5.5%
Other values (20)8081201
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)27310675
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2581251
 
9.5%
e2417185
 
8.9%
i2043154
 
7.5%
d1935078
 
7.1%
a1861735
 
6.8%
o1790590
 
6.6%
n1747209
 
6.4%
c1697208
 
6.2%
t1646782
 
6.0%
s1509282
 
5.5%
Other values (20)8081201
29.6%

SELLERPLACE_AREA
Real number (ℝ)

Skewed  Zeros 

Distinct2097
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean313.95112
Minimum-1
Maximum4000000
Zeros60523
Zeros (%)3.6%
Negative762675
Negative (%)45.7%
Memory size12.7 MiB
2025-10-19T14:48:47.763151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median3
Q382
95-th percentile1820
Maximum4000000
Range4000001
Interquartile range (IQR)83

Descriptive statistics

Standard deviation7127.4435
Coefficient of variation (CV)22.702399
Kurtosis296880.64
Mean313.95112
Median Absolute Deviation (MAD)4
Skewness529.62028
Sum5.2436555 × 108
Variance50800450
MonotonicityNot monotonic
2025-10-19T14:48:47.996085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1762675
45.7%
060523
 
3.6%
5037401
 
2.2%
3034423
 
2.1%
2033840
 
2.0%
10031409
 
1.9%
4024429
 
1.5%
2518142
 
1.1%
1517175
 
1.0%
15016652
 
1.0%
Other values (2087)633545
37.9%
ValueCountFrequency (%)
-1762675
45.7%
060523
 
3.6%
15275
 
0.3%
24374
 
0.3%
35472
 
0.3%
412797
 
0.8%
514942
 
0.9%
67411
 
0.4%
71413
 
0.1%
82022
 
0.1%
ValueCountFrequency (%)
40000005
 
< 0.1%
2560991
 
< 0.1%
2500009
 
< 0.1%
1200003
 
< 0.1%
1120004
 
< 0.1%
74625376
< 0.1%
654896
 
< 0.1%
653395
 
< 0.1%
4915136
 
< 0.1%
450001
 
< 0.1%

NAME_SELLER_INDUSTRY
Categorical

High correlation 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.7 MiB
XNA
855720 
Consumer electronics
398265 
Connectivity
276029 
Furniture
 
57849
Construction
 
29781
Other values (6)
 
52570

Length

Max length20
Median length3
Mean length9.0886252
Min length3

Characters and Unicode

Total characters15179949
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConnectivity
2nd rowXNA
3rd rowXNA
4th rowXNA
5th rowXNA

Common Values

ValueCountFrequency (%)
XNA855720
51.2%
Consumer electronics398265
23.8%
Connectivity276029
 
16.5%
Furniture57849
 
3.5%
Construction29781
 
1.8%
Clothing23949
 
1.4%
Industry19194
 
1.1%
Auto technology4990
 
0.3%
Jewelry2709
 
0.2%
MLM partners1215
 
0.1%

Length

2025-10-19T14:48:48.208880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
xna855720
41.2%
consumer398265
19.2%
electronics398265
19.2%
connectivity276029
 
13.3%
furniture57849
 
2.8%
construction29781
 
1.4%
clothing23949
 
1.2%
industry19194
 
0.9%
auto4990
 
0.2%
technology4990
 
0.2%
Other values (4)5652
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e1540296
 
10.1%
n1515347
 
10.0%
o1171553
 
7.7%
t1122072
 
7.4%
c1107330
 
7.3%
i1062415
 
7.0%
r966855
 
6.4%
A860710
 
5.7%
N855720
 
5.6%
X855720
 
5.6%
Other values (20)4121931
27.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)15179949
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1540296
 
10.1%
n1515347
 
10.0%
o1171553
 
7.7%
t1122072
 
7.4%
c1107330
 
7.3%
i1062415
 
7.0%
r966855
 
6.4%
A860710
 
5.7%
N855720
 
5.6%
X855720
 
5.6%
Other values (20)4121931
27.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)15179949
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1540296
 
10.1%
n1515347
 
10.0%
o1171553
 
7.7%
t1122072
 
7.4%
c1107330
 
7.3%
i1062415
 
7.0%
r966855
 
6.4%
A860710
 
5.7%
N855720
 
5.6%
X855720
 
5.6%
Other values (20)4121931
27.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)15179949
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1540296
 
10.1%
n1515347
 
10.0%
o1171553
 
7.7%
t1122072
 
7.4%
c1107330
 
7.3%
i1062415
 
7.0%
r966855
 
6.4%
A860710
 
5.7%
N855720
 
5.6%
X855720
 
5.6%
Other values (20)4121931
27.2%

CNT_PAYMENT
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct49
Distinct (%)< 0.1%
Missing372230
Missing (%)22.3%
Infinite0
Infinite (%)0.0%
Mean16.054082
Minimum0
Maximum84
Zeros144985
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size12.7 MiB
2025-10-19T14:48:48.412513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median12
Q324
95-th percentile48
Maximum84
Range84
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.567288
Coefficient of variation (CV)0.90738842
Kurtosis1.868014
Mean16.054082
Median Absolute Deviation (MAD)6
Skewness1.531403
Sum20837941
Variance212.20587
MonotonicityNot monotonic
2025-10-19T14:48:48.631324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
12323049
19.3%
6190461
11.4%
0144985
 
8.7%
10141851
 
8.5%
24137764
 
8.2%
1877430
 
4.6%
3672583
 
4.3%
6053600
 
3.2%
4847316
 
2.8%
830349
 
1.8%
Other values (39)78596
 
4.7%
(Missing)372230
22.3%
ValueCountFrequency (%)
0144985
8.7%
31100
 
0.1%
426924
 
1.6%
53957
 
0.2%
6190461
11.4%
71434
 
0.1%
830349
 
1.8%
91236
 
0.1%
10141851
8.5%
11669
 
< 0.1%
ValueCountFrequency (%)
8445
 
< 0.1%
72139
 
< 0.1%
6610
 
< 0.1%
6053600
3.2%
594
 
< 0.1%
542104
 
0.1%
531
 
< 0.1%
4847316
2.8%
473
 
< 0.1%
462
 
< 0.1%

NAME_YIELD_GROUP
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.7 MiB
XNA
517215 
middle
385532 
high
353331 
low_normal
322095 
low_action
92041 

Length

Max length10
Median length6
Mean length5.639709
Min length3

Characters and Unicode

Total characters9419521
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmiddle
2nd rowlow_action
3rd rowhigh
4th rowmiddle
5th rowhigh

Common Values

ValueCountFrequency (%)
XNA517215
31.0%
middle385532
23.1%
high353331
21.2%
low_normal322095
19.3%
low_action92041
 
5.5%

Length

2025-10-19T14:48:48.825537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-19T14:48:49.000229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
xna517215
31.0%
middle385532
23.1%
high353331
21.2%
low_normal322095
19.3%
low_action92041
 
5.5%

Most occurring characters

ValueCountFrequency (%)
l1121763
11.9%
i830904
 
8.8%
o828272
 
8.8%
d771064
 
8.2%
m707627
 
7.5%
h706662
 
7.5%
X517215
 
5.5%
A517215
 
5.5%
N517215
 
5.5%
a414136
 
4.4%
Other values (8)2487448
26.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)9419521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l1121763
11.9%
i830904
 
8.8%
o828272
 
8.8%
d771064
 
8.2%
m707627
 
7.5%
h706662
 
7.5%
X517215
 
5.5%
A517215
 
5.5%
N517215
 
5.5%
a414136
 
4.4%
Other values (8)2487448
26.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9419521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l1121763
11.9%
i830904
 
8.8%
o828272
 
8.8%
d771064
 
8.2%
m707627
 
7.5%
h706662
 
7.5%
X517215
 
5.5%
A517215
 
5.5%
N517215
 
5.5%
a414136
 
4.4%
Other values (8)2487448
26.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9419521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l1121763
11.9%
i830904
 
8.8%
o828272
 
8.8%
d771064
 
8.2%
m707627
 
7.5%
h706662
 
7.5%
X517215
 
5.5%
A517215
 
5.5%
N517215
 
5.5%
a414136
 
4.4%
Other values (8)2487448
26.4%

PRODUCT_COMBINATION
Categorical

High correlation 

Distinct17
Distinct (%)< 0.1%
Missing346
Missing (%)< 0.1%
Memory size12.7 MiB
Cash
285990 
POS household with interest
263622 
POS mobile with interest
220670 
Cash X-Sell: middle
143883 
Cash X-Sell: low
130248 
Other values (12)
625455 

Length

Max length30
Median length26
Mean length18.212805
Min length4

Characters and Unicode

Total characters30412981
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPOS mobile with interest
2nd rowCash X-Sell: low
3rd rowCash X-Sell: high
4th rowCash X-Sell: middle
5th rowCash Street: high

Common Values

ValueCountFrequency (%)
Cash285990
17.1%
POS household with interest263622
15.8%
POS mobile with interest220670
13.2%
Cash X-Sell: middle143883
8.6%
Cash X-Sell: low130248
7.8%
Card Street112582
 
6.7%
POS industry with interest98833
 
5.9%
POS household without interest82908
 
5.0%
Card X-Sell80582
 
4.8%
Cash Street: high59639
 
3.6%
Other values (7)190911
11.4%

Length

2025-10-19T14:48:49.197998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cash747553
15.0%
pos729151
14.7%
interest729151
14.7%
with607004
12.2%
x-sell414014
8.3%
household346530
7.0%
mobile244752
 
4.9%
street240713
 
4.8%
card193164
 
3.9%
middle178541
 
3.6%
Other values (6)543038
10.9%

Most occurring characters

ValueCountFrequency (%)
3303743
 
10.9%
e3149999
 
10.4%
t2928895
 
9.6%
h2434078
 
8.0%
i2111970
 
6.9%
s1937224
 
6.4%
l1761933
 
5.8%
S1383878
 
4.6%
r1300897
 
4.3%
o1250475
 
4.1%
Other values (15)8849889
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)30412981
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3303743
 
10.9%
e3149999
 
10.4%
t2928895
 
9.6%
h2434078
 
8.0%
i2111970
 
6.9%
s1937224
 
6.4%
l1761933
 
5.8%
S1383878
 
4.6%
r1300897
 
4.3%
o1250475
 
4.1%
Other values (15)8849889
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)30412981
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3303743
 
10.9%
e3149999
 
10.4%
t2928895
 
9.6%
h2434078
 
8.0%
i2111970
 
6.9%
s1937224
 
6.4%
l1761933
 
5.8%
S1383878
 
4.6%
r1300897
 
4.3%
o1250475
 
4.1%
Other values (15)8849889
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)30412981
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3303743
 
10.9%
e3149999
 
10.4%
t2928895
 
9.6%
h2434078
 
8.0%
i2111970
 
6.9%
s1937224
 
6.4%
l1761933
 
5.8%
S1383878
 
4.6%
r1300897
 
4.3%
o1250475
 
4.1%
Other values (15)8849889
29.1%

DAYS_FIRST_DRAWING
Real number (ℝ)

High correlation  Missing 

Distinct2838
Distinct (%)0.3%
Missing673065
Missing (%)40.3%
Infinite0
Infinite (%)0.0%
Mean342209.86
Minimum-2922
Maximum365243
Zeros0
Zeros (%)0.0%
Negative62705
Negative (%)3.8%
Memory size12.7 MiB
2025-10-19T14:48:49.382293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2922
5-th percentile-269
Q1365243
median365243
Q3365243
95-th percentile365243
Maximum365243
Range368165
Interquartile range (IQR)0

Descriptive statistics

Standard deviation88916.116
Coefficient of variation (CV)0.25982921
Kurtosis10.969807
Mean342209.86
Median Absolute Deviation (MAD)0
Skewness-3.6013428
Sum3.4123421 × 1011
Variance7.9060757 × 109
MonotonicityNot monotonic
2025-10-19T14:48:49.589299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
365243934444
55.9%
-228123
 
< 0.1%
-212121
 
< 0.1%
-224121
 
< 0.1%
-223119
 
< 0.1%
-220118
 
< 0.1%
-210117
 
< 0.1%
-235117
 
< 0.1%
-240116
 
< 0.1%
-226115
 
< 0.1%
Other values (2828)61638
 
3.7%
(Missing)673065
40.3%
ValueCountFrequency (%)
-29221
 
< 0.1%
-29212
 
< 0.1%
-29205
< 0.1%
-291912
< 0.1%
-29187
< 0.1%
-29175
< 0.1%
-29166
< 0.1%
-29157
< 0.1%
-29144
 
< 0.1%
-29139
< 0.1%
ValueCountFrequency (%)
365243934444
55.9%
-220
 
< 0.1%
-314
 
< 0.1%
-410
 
< 0.1%
-514
 
< 0.1%
-620
 
< 0.1%
-720
 
< 0.1%
-815
 
< 0.1%
-921
 
< 0.1%
-1022
 
< 0.1%

DAYS_FIRST_DUE
Real number (ℝ)

High correlation  Missing 

Distinct2892
Distinct (%)0.3%
Missing673065
Missing (%)40.3%
Infinite0
Infinite (%)0.0%
Mean13826.269
Minimum-2892
Maximum365243
Zeros0
Zeros (%)0.0%
Negative956504
Negative (%)57.3%
Memory size12.7 MiB
2025-10-19T14:48:49.794972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2892
5-th percentile-2608
Q1-1628
median-831
Q3-411
95-th percentile-48
Maximum365243
Range368135
Interquartile range (IQR)1217

Descriptive statistics

Standard deviation72444.87
Coefficient of variation (CV)5.2396542
Kurtosis19.570596
Mean13826.269
Median Absolute Deviation (MAD)525
Skewness4.6440959
Sum1.3786851 × 1010
Variance5.2482591 × 109
MonotonicityNot monotonic
2025-10-19T14:48:50.015289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36524340645
 
2.4%
-334772
 
< 0.1%
-509760
 
< 0.1%
-208751
 
< 0.1%
-330750
 
< 0.1%
-292746
 
< 0.1%
-691745
 
< 0.1%
-270744
 
< 0.1%
-299744
 
< 0.1%
-327743
 
< 0.1%
Other values (2882)949749
56.9%
(Missing)673065
40.3%
ValueCountFrequency (%)
-28929
 
< 0.1%
-289155
< 0.1%
-289073
< 0.1%
-288986
< 0.1%
-288896
< 0.1%
-288787
< 0.1%
-288692
< 0.1%
-2885121
< 0.1%
-2884113
< 0.1%
-2883136
< 0.1%
ValueCountFrequency (%)
36524340645
2.4%
-214
 
< 0.1%
-3136
 
< 0.1%
-4132
 
< 0.1%
-5182
 
< 0.1%
-6175
 
< 0.1%
-7157
 
< 0.1%
-8156
 
< 0.1%
-9176
 
< 0.1%
-10168
 
< 0.1%

DAYS_LAST_DUE_1ST_VERSION
Real number (ℝ)

High correlation  Missing 

Distinct4605
Distinct (%)0.5%
Missing673065
Missing (%)40.3%
Infinite0
Infinite (%)0.0%
Mean33767.774
Minimum-2801
Maximum365243
Zeros705
Zeros (%)< 0.1%
Negative678188
Negative (%)40.6%
Memory size12.7 MiB
2025-10-19T14:48:50.228146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2801
5-th percentile-2327
Q1-1242
median-361
Q3129
95-th percentile365243
Maximum365243
Range368044
Interquartile range (IQR)1371

Descriptive statistics

Standard deviation106857.03
Coefficient of variation (CV)3.1644678
Kurtosis5.7261481
Mean33767.774
Median Absolute Deviation (MAD)639
Skewness2.7794499
Sum3.3671502 × 1010
Variance1.1418426 × 1010
MonotonicityNot monotonic
2025-10-19T14:48:50.436150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36524393864
 
5.6%
9720
 
< 0.1%
8706
 
< 0.1%
0705
 
< 0.1%
5702
 
< 0.1%
10698
 
< 0.1%
2688
 
< 0.1%
6685
 
< 0.1%
1685
 
< 0.1%
-1675
 
< 0.1%
Other values (4595)897021
53.7%
(Missing)673065
40.3%
ValueCountFrequency (%)
-28019
 
< 0.1%
-28009
 
< 0.1%
-27996
 
< 0.1%
-279818
< 0.1%
-279711
< 0.1%
-279624
< 0.1%
-279514
< 0.1%
-279417
< 0.1%
-279322
< 0.1%
-279211
< 0.1%
ValueCountFrequency (%)
36524393864
5.6%
23891
 
< 0.1%
20981
 
< 0.1%
20901
 
< 0.1%
20321
 
< 0.1%
20161
 
< 0.1%
20111
 
< 0.1%
19931
 
< 0.1%
19901
 
< 0.1%
19541
 
< 0.1%

DAYS_LAST_DUE
Real number (ℝ)

High correlation  Missing 

Distinct2873
Distinct (%)0.3%
Missing673065
Missing (%)40.3%
Infinite0
Infinite (%)0.0%
Mean76582.403
Minimum-2889
Maximum365243
Zeros0
Zeros (%)0.0%
Negative785928
Negative (%)47.1%
Memory size12.7 MiB
2025-10-19T14:48:50.642402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2889
5-th percentile-2349
Q1-1314
median-537
Q3-74
95-th percentile365243
Maximum365243
Range368132
Interquartile range (IQR)1240

Descriptive statistics

Standard deviation149647.42
Coefficient of variation (CV)1.9540705
Kurtosis-0.01044733
Mean76582.403
Median Absolute Deviation (MAD)618
Skewness1.4104726
Sum7.6364067 × 1010
Variance2.2394349 × 1010
MonotonicityNot monotonic
2025-10-19T14:48:50.847090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
365243211221
 
12.6%
-245658
 
< 0.1%
-188650
 
< 0.1%
-239642
 
< 0.1%
-167638
 
< 0.1%
-247629
 
< 0.1%
-305627
 
< 0.1%
-268624
 
< 0.1%
-236623
 
< 0.1%
-160623
 
< 0.1%
Other values (2863)780214
46.7%
(Missing)673065
40.3%
ValueCountFrequency (%)
-28891
 
< 0.1%
-28881
 
< 0.1%
-28851
 
< 0.1%
-28842
< 0.1%
-28833
< 0.1%
-28811
 
< 0.1%
-28783
< 0.1%
-28761
 
< 0.1%
-28692
< 0.1%
-28671
 
< 0.1%
ValueCountFrequency (%)
365243211221
12.6%
-230
 
< 0.1%
-3402
 
< 0.1%
-4501
 
< 0.1%
-5444
 
< 0.1%
-6531
 
< 0.1%
-7559
 
< 0.1%
-8548
 
< 0.1%
-9568
 
< 0.1%
-10569
 
< 0.1%

DAYS_TERMINATION
Real number (ℝ)

High correlation  Missing 

Distinct2830
Distinct (%)0.3%
Missing673065
Missing (%)40.3%
Infinite0
Infinite (%)0.0%
Mean81992.344
Minimum-2874
Maximum365243
Zeros0
Zeros (%)0.0%
Negative771236
Negative (%)46.2%
Memory size12.7 MiB
2025-10-19T14:48:51.061985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2874
5-th percentile-2331
Q1-1270
median-499
Q3-44
95-th percentile365243
Maximum365243
Range368117
Interquartile range (IQR)1226

Descriptive statistics

Standard deviation153303.52
Coefficient of variation (CV)1.8697297
Kurtosis-0.29327669
Mean81992.344
Median Absolute Deviation (MAD)672
Skewness1.306376
Sum8.1758584 × 1010
Variance2.3501968 × 1010
MonotonicityNot monotonic
2025-10-19T14:48:51.268559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
365243225913
 
13.5%
-233786
 
< 0.1%
-170770
 
< 0.1%
-184770
 
< 0.1%
-163769
 
< 0.1%
-169760
 
< 0.1%
-303754
 
< 0.1%
-177753
 
< 0.1%
-305742
 
< 0.1%
-212741
 
< 0.1%
Other values (2820)764391
45.8%
(Missing)673065
40.3%
ValueCountFrequency (%)
-28741
 
< 0.1%
-28701
 
< 0.1%
-28651
 
< 0.1%
-28521
 
< 0.1%
-28481
 
< 0.1%
-28471
 
< 0.1%
-28452
< 0.1%
-28441
 
< 0.1%
-28393
< 0.1%
-28371
 
< 0.1%
ValueCountFrequency (%)
365243225913
13.5%
-2602
 
< 0.1%
-3597
 
< 0.1%
-4636
 
< 0.1%
-5638
 
< 0.1%
-6447
 
< 0.1%
-7223
 
< 0.1%
-8688
 
< 0.1%
-9711
 
< 0.1%
-10605
 
< 0.1%

NFLAG_INSURED_ON_APPROVAL
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing673065
Missing (%)40.3%
Memory size12.7 MiB
0.0
665527 
1.0
331622 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2991447
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0665527
39.8%
1.0331622
19.9%
(Missing)673065
40.3%

Length

2025-10-19T14:48:51.461781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-19T14:48:51.593430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0665527
66.7%
1.0331622
33.3%

Most occurring characters

ValueCountFrequency (%)
01662676
55.6%
.997149
33.3%
1331622
 
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2991447
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01662676
55.6%
.997149
33.3%
1331622
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2991447
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01662676
55.6%
.997149
33.3%
1331622
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2991447
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01662676
55.6%
.997149
33.3%
1331622
 
11.1%

Interactions

2025-10-19T14:48:05.997503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:23.741631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:30.160902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:36.446944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:42.933784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:49.068750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:55.458328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:00.275264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:06.616211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:12.798437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:17.685043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:21.175785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:24.498400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:30.879277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:37.631696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:43.477627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:49.004323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:54.411261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:59.886488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:48:06.330529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:24.131576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:30.539712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:36.831322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:43.323384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:49.470633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:55.734359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:00.653245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:07.001512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:13.076274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:17.847627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:21.342751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:24.889292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:31.258865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:38.029987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:43.794715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:49.305064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:54.722296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:48:00.195722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:48:06.641057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:24.501867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:30.927608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:37.197443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:43.723623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:50.125006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:56.000426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:01.038234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:07.390017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:13.354803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:18.020124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:21.519947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:25.291786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:31.643534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:38.401040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:44.111450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:49.612678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:55.036049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:48:00.501404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:48:06.959533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:24.865232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:31.282815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:37.558332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:44.091073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:50.494633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:56.262586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:01.366558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:07.785828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:13.629986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:18.167492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:21.672996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:25.701148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:32.004410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:38.735828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:44.435193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:49.919595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:55.342824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:48:00.807575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:48:07.261266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:25.263802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:31.730413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2025-10-19T14:47:29.235052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:35.347793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:41.925668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:47.423595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:52.880471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:58.347365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:48:04.443622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:48:10.302956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:28.804038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:35.121552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:41.533470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:47.745859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:54.291939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:59.194643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:05.276054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:11.585668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:16.654322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:20.505499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:23.617456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:29.559694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:35.666229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:42.239531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:47.762668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:53.170302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:58.653037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:48:04.757195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:48:10.611828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:29.120950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:35.435592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:41.861986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:48.053725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:54.596162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:59.436066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:05.591072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:11.894909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:16.931291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:20.679374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:23.785491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:29.879667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:35.994526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:42.552770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:48.073251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:53.486162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:58.948002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:48:05.073276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:48:10.928060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:29.442053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:35.754658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:42.194353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:48.361950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:54.893410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:59.676356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:05.910667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:12.214385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:17.207038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:20.850441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:23.954798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:30.202857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:36.317947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:42.868132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:48.390020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:53.800619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:59.260204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:48:05.370839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:48:11.221195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:29.764328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:36.069880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:42.537092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:48.671987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:55.189374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:46:59.915398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:06.227689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:12.525806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:17.489079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:21.017704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:24.127874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:30.526534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:37.212087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:43.179127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:48.700363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:54.109272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:47:59.580547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-19T14:48:05.685512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2025-10-19T14:48:51.759694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AMT_ANNUITYAMT_APPLICATIONAMT_CREDITAMT_DOWN_PAYMENTAMT_GOODS_PRICECHANNEL_TYPECNT_PAYMENTCODE_REJECT_REASONDAYS_DECISIONDAYS_FIRST_DRAWINGDAYS_FIRST_DUEDAYS_LAST_DUEDAYS_LAST_DUE_1ST_VERSIONDAYS_TERMINATIONFLAG_LAST_APPL_PER_CONTRACTHOUR_APPR_PROCESS_STARTNAME_CASH_LOAN_PURPOSENAME_CLIENT_TYPENAME_CONTRACT_STATUSNAME_CONTRACT_TYPENAME_GOODS_CATEGORYNAME_PAYMENT_TYPENAME_PORTFOLIONAME_PRODUCT_TYPENAME_SELLER_INDUSTRYNAME_TYPE_SUITENAME_YIELD_GROUPNFLAG_INSURED_ON_APPROVALNFLAG_LAST_APPL_IN_DAYPRODUCT_COMBINATIONRATE_DOWN_PAYMENTRATE_INTEREST_PRIMARYRATE_INTEREST_PRIVILEGEDSELLERPLACE_AREASK_ID_CURRSK_ID_PREVWEEKDAY_APPR_PROCESS_START
AMT_ANNUITY1.0000.8300.8800.0440.8890.1020.3920.0460.2820.0700.2320.2730.2720.2500.014-0.0490.1000.0620.0620.2020.0800.0240.1650.1710.0890.0370.0740.1630.0110.116-0.178-0.092-0.186-0.4130.0010.0130.020
AMT_APPLICATION0.8301.0000.9170.0261.0000.1180.6380.091-0.1220.1930.2760.3310.3050.2780.0060.0140.1010.0590.1100.1360.0700.0550.2060.2320.0730.0470.1490.1390.0030.183-0.198-0.128-0.2380.0480.001-0.0120.018
AMT_CREDIT0.8800.9171.000-0.0950.9850.1040.5490.104-0.155-0.1050.3090.4350.5080.4380.0180.0030.1250.0670.1210.1530.0890.0600.2050.2650.0950.0530.1590.1730.0160.225-0.327-0.086-0.184-0.0090.000-0.0120.020
AMT_DOWN_PAYMENT0.0440.026-0.0951.0000.0260.189-0.2220.028-0.234-0.001-0.225-0.265-0.265-0.2620.0000.0340.0000.0030.0290.0020.0190.0000.2310.0020.0270.0000.0100.0060.0000.0140.918-0.277-0.4090.0550.003-0.0040.001
AMT_GOODS_PRICE0.8891.0000.9850.0261.0000.1550.6000.0950.355-0.0390.2970.4080.4770.4050.016-0.0590.1260.0840.1210.2360.1040.0100.1970.2340.1120.0470.1360.1340.0100.180-0.198-0.128-0.238-0.4560.0010.0150.025
CHANNEL_TYPE0.1020.1180.1040.1890.1551.0000.2240.1170.1870.2700.1130.2670.2990.2800.0610.0780.3000.2430.3310.5240.3930.2520.6560.4090.4370.1490.2600.4980.0460.4240.0900.4320.5920.0020.0030.0190.076
CNT_PAYMENT0.3920.6380.549-0.2220.6000.2241.0000.1260.1750.4120.0510.1490.1260.0970.122-0.0480.2240.1340.1930.5700.1790.2090.4900.3650.1780.0830.3270.3340.0870.320-0.3350.072-0.055-0.255-0.0000.0100.041
CODE_REJECT_REASON0.0460.0910.1040.0280.0950.1170.1261.0000.1000.0000.0000.0000.0000.0000.3010.0200.1180.1080.8160.1670.1140.0920.2080.2780.1110.0560.1240.0000.2350.1860.0670.1090.0000.0000.0020.0120.021
DAYS_DECISION0.282-0.122-0.155-0.2340.3550.1870.1750.1001.000-0.0270.9740.8970.7950.8490.045-0.0430.1310.1870.3170.2580.1670.2380.2910.1620.1660.1080.3040.2520.0390.239-0.2940.4550.638-0.404-0.0000.0230.033
DAYS_FIRST_DRAWING0.0700.193-0.105-0.001-0.0390.2700.4120.000-0.0271.000-0.042-0.184-0.406-0.3191.0000.0140.1650.1281.0000.8040.3250.5140.8040.3340.2240.0810.8040.1780.0000.804-0.001NaNNaN0.162-0.001-0.0010.042
DAYS_FIRST_DUE0.2320.2760.309-0.2250.2970.1130.0510.0000.974-0.0421.0000.9180.8280.8681.000-0.0000.1270.1001.0000.5120.1900.3300.5120.2410.1080.0460.5120.1190.0020.513-0.2880.4620.651-0.173-0.0000.0020.019
DAYS_LAST_DUE0.2730.3310.435-0.2650.4080.2670.1490.0000.897-0.1840.9181.0000.9030.9631.000-0.0130.0530.1881.0000.4410.3080.2610.4410.3010.2770.1380.4640.0130.0020.492-0.3420.4440.605-0.2330.0010.0030.045
DAYS_LAST_DUE_1ST_VERSION0.2720.3050.508-0.2650.4770.2990.1260.0000.795-0.4060.8280.9031.0000.9421.000-0.0290.2060.1501.0000.9990.4050.6390.9990.4320.2680.0900.9990.2230.0010.999-0.3430.4610.647-0.3180.0000.0030.049
DAYS_TERMINATION0.2500.2780.438-0.2620.4050.2800.0970.0000.849-0.3190.8680.9630.9421.0001.000-0.0150.0380.1971.0000.5070.3300.3060.5070.3220.2770.1360.5270.0030.0000.550-0.3390.4420.602-0.2370.0010.0030.047
FLAG_LAST_APPL_PER_CONTRACT0.0140.0060.0180.0000.0160.0610.1220.3010.0451.0001.0001.0001.0001.0001.0000.0080.0640.0340.1560.1970.0620.0920.2310.1160.0290.0200.1071.0000.7220.2490.0001.0001.0000.0000.0020.0070.009
HOUR_APPR_PROCESS_START-0.0490.0140.0030.034-0.0590.078-0.0480.020-0.0430.014-0.000-0.013-0.029-0.0150.0081.0000.0470.0430.0490.0900.0580.0610.0720.0720.0500.0360.0410.1190.0060.0600.029-0.051-0.0590.1390.003-0.0030.025
NAME_CASH_LOAN_PURPOSE0.1000.1010.1250.0000.1260.3000.2240.1180.1310.1650.1270.0530.2060.0380.0640.0471.0000.2130.2960.5770.1600.1330.4720.5690.2410.1310.1410.6360.0450.3120.0751.0001.0000.0000.0040.0090.066
NAME_CLIENT_TYPE0.0620.0590.0670.0030.0840.2430.1340.1080.1870.1280.1000.1880.1500.1970.0340.0430.2131.0000.1870.2550.2570.1210.2640.2050.2410.1050.1660.1760.0240.2650.0860.0260.1520.0020.0040.0110.060
NAME_CONTRACT_STATUS0.0620.1100.1210.0290.1210.3310.1930.8160.3171.0001.0001.0001.0001.0000.1560.0490.2960.1871.0000.2970.3150.3610.5500.3110.3270.0750.4311.0000.1100.5560.1001.0001.0000.0000.0010.0310.058
NAME_CONTRACT_TYPE0.2020.1360.1530.0020.2360.5240.5700.1670.2580.8040.5120.4410.9990.5070.1970.0900.5770.2550.2971.0000.5700.3290.7410.4810.5220.2520.3740.6420.1431.0000.1611.0001.0000.0010.0030.0170.110
NAME_GOODS_CATEGORY0.0800.0700.0890.0190.1040.3930.1790.1140.1670.3250.1900.3080.4050.3300.0620.0580.1600.2570.3150.5701.0000.2640.4750.4640.7280.1530.3320.4660.0460.4370.0930.2730.3150.0290.0030.0100.081
NAME_PAYMENT_TYPE0.0240.0550.0600.0000.0100.2520.2090.0920.2380.5140.3300.2610.6390.3060.0920.0610.1330.1210.3610.3290.2641.0000.4430.0550.2320.0420.4420.1950.0640.4720.0250.2440.3810.0000.0030.0170.048
NAME_PORTFOLIO0.1650.2060.2050.2310.1970.6560.4900.2080.2910.8040.5120.4410.9990.5070.2310.0720.4720.2640.5500.7410.4750.4431.0000.7130.4410.2050.5050.6420.1690.8060.1531.0001.0000.0010.0030.0210.093
NAME_PRODUCT_TYPE0.1710.2320.2650.0020.2340.4090.3650.2780.1620.3340.2410.3010.4320.3220.1160.0720.5690.2050.3110.4810.4640.0550.7131.0000.3750.2510.1580.4730.0890.8880.1591.0001.0000.0010.0030.0060.090
NAME_SELLER_INDUSTRY0.0890.0730.0950.0270.1120.4370.1780.1110.1660.2240.1080.2770.2680.2770.0290.0500.2410.2410.3270.5220.7280.2320.4410.3751.0000.1550.3120.4780.0190.5600.0870.2640.3220.0080.0020.0100.077
NAME_TYPE_SUITE0.0370.0470.0530.0000.0470.1490.0830.0560.1080.0810.0460.1380.0900.1360.0200.0360.1310.1050.0750.2520.1530.0420.2050.2510.1551.0000.0600.1320.0160.1560.0330.0000.0000.0000.0020.0050.056
NAME_YIELD_GROUP0.0740.1490.1590.0100.1360.2600.3270.1240.3040.8040.5120.4640.9990.5270.1070.0410.1410.1660.4310.3740.3320.4420.5050.1580.3120.0601.0000.2560.0740.7650.1681.0001.0000.0050.0020.0160.044
NFLAG_INSURED_ON_APPROVAL0.1630.1390.1730.0060.1340.4980.3340.0000.2520.1780.1190.0130.2230.0031.0000.1190.6360.1761.0000.6420.4660.1950.6420.4730.4780.1320.2561.0000.0070.6590.0540.3370.1860.0000.0040.0060.086
NFLAG_LAST_APPL_IN_DAY0.0110.0030.0160.0000.0100.0460.0870.2350.0390.0000.0020.0020.0010.0000.7220.0060.0450.0240.1100.1430.0460.0640.1690.0890.0190.0160.0740.0071.0000.1780.0060.0000.0150.0000.0020.0040.022
PRODUCT_COMBINATION0.1160.1830.2250.0140.1800.4240.3200.1860.2390.8040.5130.4920.9990.5500.2490.0600.3120.2650.5561.0000.4370.4720.8060.8880.5600.1560.7650.6590.1781.0000.1210.3800.5110.0160.0030.0130.081
RATE_DOWN_PAYMENT-0.178-0.198-0.3270.918-0.1980.090-0.3350.067-0.294-0.001-0.288-0.342-0.343-0.3390.0000.0290.0750.0860.1000.1610.0930.0250.1530.1590.0870.0330.1680.0540.0060.1211.000-0.237-0.318-0.0230.002-0.0050.010
RATE_INTEREST_PRIMARY-0.092-0.128-0.086-0.277-0.1280.4320.0720.1090.455NaN0.4620.4440.4610.4421.000-0.0511.0000.0261.0001.0000.2730.2441.0001.0000.2640.0001.0000.3370.0000.380-0.2371.0000.782-0.047-0.002-0.0090.041
RATE_INTEREST_PRIVILEGED-0.186-0.238-0.184-0.409-0.2380.592-0.0550.0000.638NaN0.6510.6050.6470.6021.000-0.0591.0000.1521.0001.0000.3150.3811.0001.0000.3220.0001.0000.1860.0150.511-0.3180.7821.000-0.094-0.029-0.0190.027
SELLERPLACE_AREA-0.4130.048-0.0090.055-0.4560.002-0.2550.000-0.4040.162-0.173-0.233-0.318-0.2370.0000.1390.0000.0020.0000.0010.0290.0000.0010.0010.0080.0000.0050.0000.0000.016-0.023-0.047-0.0941.000-0.001-0.0270.001
SK_ID_CURR0.0010.0010.0000.0030.0010.003-0.0000.002-0.000-0.001-0.0000.0010.0000.0010.0020.0030.0040.0040.0010.0030.0030.0030.0030.0030.0020.0020.0020.0040.0020.0030.002-0.002-0.029-0.0011.000-0.0000.002
SK_ID_PREV0.013-0.012-0.012-0.0040.0150.0190.0100.0120.023-0.0010.0020.0030.0030.0030.007-0.0030.0090.0110.0310.0170.0100.0170.0210.0060.0100.0050.0160.0060.0040.013-0.005-0.009-0.019-0.027-0.0001.0000.002
WEEKDAY_APPR_PROCESS_START0.0200.0180.0200.0010.0250.0760.0410.0210.0330.0420.0190.0450.0490.0470.0090.0250.0660.0600.0580.1100.0810.0480.0930.0900.0770.0560.0440.0860.0220.0810.0100.0410.0270.0010.0020.0021.000

Missing values

2025-10-19T14:48:12.959664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-19T14:48:20.166020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-10-19T14:48:33.397772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

SK_ID_PREVSK_ID_CURRNAME_CONTRACT_TYPEAMT_ANNUITYAMT_APPLICATIONAMT_CREDITAMT_DOWN_PAYMENTAMT_GOODS_PRICEWEEKDAY_APPR_PROCESS_STARTHOUR_APPR_PROCESS_STARTFLAG_LAST_APPL_PER_CONTRACTNFLAG_LAST_APPL_IN_DAYRATE_DOWN_PAYMENTRATE_INTEREST_PRIMARYRATE_INTEREST_PRIVILEGEDNAME_CASH_LOAN_PURPOSENAME_CONTRACT_STATUSDAYS_DECISIONNAME_PAYMENT_TYPECODE_REJECT_REASONNAME_TYPE_SUITENAME_CLIENT_TYPENAME_GOODS_CATEGORYNAME_PORTFOLIONAME_PRODUCT_TYPECHANNEL_TYPESELLERPLACE_AREANAME_SELLER_INDUSTRYCNT_PAYMENTNAME_YIELD_GROUPPRODUCT_COMBINATIONDAYS_FIRST_DRAWINGDAYS_FIRST_DUEDAYS_LAST_DUE_1ST_VERSIONDAYS_LAST_DUEDAYS_TERMINATIONNFLAG_INSURED_ON_APPROVAL
02030495271877Consumer loans1730.43017145.017145.00.017145.0SATURDAY15Y10.00.1828320.867336XAPApproved-73Cash through the bankXAPNaNRepeaterMobilePOSXNACountry-wide35Connectivity12.0middlePOS mobile with interest365243.0-42.0300.0-42.0-37.00.0
12802425108129Cash loans25188.615607500.0679671.0NaN607500.0THURSDAY11Y1NaNNaNNaNXNAApproved-164XNAXAPUnaccompaniedRepeaterXNACashx-sellContact center-1XNA36.0low_actionCash X-Sell: low365243.0-134.0916.0365243.0365243.01.0
22523466122040Cash loans15060.735112500.0136444.5NaN112500.0TUESDAY11Y1NaNNaNNaNXNAApproved-301Cash through the bankXAPSpouse, partnerRepeaterXNACashx-sellCredit and cash offices-1XNA12.0highCash X-Sell: high365243.0-271.059.0365243.0365243.01.0
32819243176158Cash loans47041.335450000.0470790.0NaN450000.0MONDAY7Y1NaNNaNNaNXNAApproved-512Cash through the bankXAPNaNRepeaterXNACashx-sellCredit and cash offices-1XNA12.0middleCash X-Sell: middle365243.0-482.0-152.0-182.0-177.01.0
41784265202054Cash loans31924.395337500.0404055.0NaN337500.0THURSDAY9Y1NaNNaNNaNRepairsRefused-781Cash through the bankHCNaNRepeaterXNACashwalk-inCredit and cash offices-1XNA24.0highCash Street: highNaNNaNNaNNaNNaNNaN
51383531199383Cash loans23703.930315000.0340573.5NaN315000.0SATURDAY8Y1NaNNaNNaNEveryday expensesApproved-684Cash through the bankXAPFamilyRepeaterXNACashx-sellCredit and cash offices-1XNA18.0low_normalCash X-Sell: low365243.0-654.0-144.0-144.0-137.01.0
62315218175704Cash loansNaN0.00.0NaNNaNTUESDAY11Y1NaNNaNNaNXNACanceled-14XNAXAPNaNRepeaterXNAXNAXNACredit and cash offices-1XNANaNXNACashNaNNaNNaNNaNNaNNaN
71656711296299Cash loansNaN0.00.0NaNNaNMONDAY7Y1NaNNaNNaNXNACanceled-21XNAXAPNaNRepeaterXNAXNAXNACredit and cash offices-1XNANaNXNACashNaNNaNNaNNaNNaNNaN
82367563342292Cash loansNaN0.00.0NaNNaNMONDAY15Y1NaNNaNNaNXNACanceled-386XNAXAPNaNRepeaterXNAXNAXNACredit and cash offices-1XNANaNXNACashNaNNaNNaNNaNNaNNaN
92579447334349Cash loansNaN0.00.0NaNNaNSATURDAY15Y1NaNNaNNaNXNACanceled-57XNAXAPNaNRepeaterXNAXNAXNACredit and cash offices-1XNANaNXNACashNaNNaNNaNNaNNaNNaN
SK_ID_PREVSK_ID_CURRNAME_CONTRACT_TYPEAMT_ANNUITYAMT_APPLICATIONAMT_CREDITAMT_DOWN_PAYMENTAMT_GOODS_PRICEWEEKDAY_APPR_PROCESS_STARTHOUR_APPR_PROCESS_STARTFLAG_LAST_APPL_PER_CONTRACTNFLAG_LAST_APPL_IN_DAYRATE_DOWN_PAYMENTRATE_INTEREST_PRIMARYRATE_INTEREST_PRIVILEGEDNAME_CASH_LOAN_PURPOSENAME_CONTRACT_STATUSDAYS_DECISIONNAME_PAYMENT_TYPECODE_REJECT_REASONNAME_TYPE_SUITENAME_CLIENT_TYPENAME_GOODS_CATEGORYNAME_PORTFOLIONAME_PRODUCT_TYPECHANNEL_TYPESELLERPLACE_AREANAME_SELLER_INDUSTRYCNT_PAYMENTNAME_YIELD_GROUPPRODUCT_COMBINATIONDAYS_FIRST_DRAWINGDAYS_FIRST_DUEDAYS_LAST_DUE_1ST_VERSIONDAYS_LAST_DUEDAYS_TERMINATIONNFLAG_INSURED_ON_APPROVAL
16702041407146198989Cash loans36598.095450000.0570073.5NaN450000.0THURSDAY12Y1NaNNaNNaNXNARefused-848Cash through the bankHCUnaccompaniedRepeaterXNACashx-sellCredit and cash offices100XNA24.0middleCash X-Sell: middleNaNNaNNaNNaNNaNNaN
16702052815130338803Cash loans14584.050135000.0182956.5NaN135000.0SATURDAY10Y1NaNNaNNaNXNARefused-1407Cash through the bankLIMITUnaccompaniedRepeaterXNACashwalk-inCredit and cash offices100XNA24.0highCash Street: highNaNNaNNaNNaNNaNNaN
16702062459206238591Cash loans19401.435180000.0243936.00.0180000.0TUESDAY13Y10.000000NaNNaNPurchase of electronic equipmentApproved-1833Cash through the bankXAPUnaccompaniedNewXNACashwalk-inCredit and cash offices100XNA24.0highCash Street: high365243.0-1802.0-1112.0-1112.0-1100.00.0
16702071662353443544Cash loans12607.875112500.0112500.00.0112500.0MONDAY10Y10.000000NaNNaNXNARefused-2514Cash through the bankSCOUnaccompaniedRepeaterXNACashwalk-inCredit and cash offices100XNA12.0highCash Street: highNaNNaNNaNNaNNaNNaN
16702081556789209732Cash loans22299.390315000.0436216.5NaN315000.0THURSDAY17Y1NaNNaNNaNXNAApproved-1279Cash through the bankXAPUnaccompaniedRefreshedXNACashx-sellCredit and cash offices100XNA36.0middleCash X-Sell: middle365243.0-1249.0-199.0-919.0-912.01.0
16702092300464352015Consumer loans14704.290267295.5311400.00.0267295.5WEDNESDAY12Y10.000000NaNNaNXAPApproved-544Cash through the bankXAPNaNRefreshedFurniturePOSXNAStone43Furniture30.0low_normalPOS industry with interest365243.0-508.0362.0-358.0-351.00.0
16702102357031334635Consumer loans6622.02087750.064291.529250.087750.0TUESDAY15Y10.340554NaNNaNXAPApproved-1694Cash through the bankXAPUnaccompaniedNewFurniturePOSXNAStone43Furniture12.0middlePOS industry with interest365243.0-1604.0-1274.0-1304.0-1297.00.0
16702112659632249544Consumer loans11520.855105237.0102523.510525.5105237.0MONDAY12Y10.101401NaNNaNXAPApproved-1488Cash through the bankXAPSpouse, partnerRepeaterConsumer ElectronicsPOSXNACountry-wide1370Consumer electronics10.0low_normalPOS household with interest365243.0-1457.0-1187.0-1187.0-1181.00.0
16702122785582400317Cash loans18821.520180000.0191880.0NaN180000.0WEDNESDAY9Y1NaNNaNNaNXNAApproved-1185Cash through the bankXAPFamilyRepeaterXNACashx-sellAP+ (Cash loan)-1XNA12.0low_normalCash X-Sell: low365243.0-1155.0-825.0-825.0-817.01.0
16702132418762261212Cash loans16431.300360000.0360000.0NaN360000.0SUNDAY10Y1NaNNaNNaNXNAApproved-1193Cash through the bankXAPFamilyRepeaterXNACashx-sellAP+ (Cash loan)-1XNA48.0middleCash X-Sell: middle365243.0-1163.0247.0-443.0-423.00.0